DryBeanFinal
Devon
May 6, 2016
Agglomerative, Hierarchical Clustering based on Standardized Data, Goodness of fit
library(scales)
library(ggplot2)
library(plyr)
library(vegan)## Loading required package: permute## Loading required package: lattice## This is vegan 2.3-4library(cluster)
library(ade4)##
## Attaching package: 'ade4'## The following object is masked from 'package:vegan':
##
## ccalibrary(stats)
#install.packages("ade4")
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & !(Y$Var=="Z"))
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwghttrial),
meanwght = mean(rtwghttrial, na.rm=TRUE),
meanlngth = mean(rtlngthtrial, na.rm=TRUE),
meanarea = mean(rtareatrial, na.rm=TRUE),
meanemerge = mean(emergetrial, na.rm=TRUE)))
Dend<-scale(as.matrix(Y4 [,c("meanwght", "meanlngth", "meanarea", "meanemerge")]))
rownames(Dend)=Y4$Title
bray <- (dist(Dend, method = "maximum", diag = FALSE, upper = FALSE))
bray.v<-as.vector(bray)
bray.dis <- agnes(bray, method="average", diss = TRUE)
bray.disv<-as.vector(bray.dis)
# very simple dendrogram
plot(bray.dis)
#Cophenetic Distances and Correlation
bray.ave.co <- cophenetic(bray.dis)
# vectorize the cophenetic distances
bray.ave.cov <- as.vector(bray.ave.co)
cor(bray.ave.cov, bray.v)## [1] 0.927242plot(y = bray.ave.cov, x = bray.v)
#Mantel Test
mant1 <- mantel.rtest(bray, bray.ave.co, nrepet=1000)
plot(mant1)## Warning in hist.default(sim, plot = FALSE, nclass = nclass, xlim = xlim0):
## argument 'xlim' is not made use of
summary(mant1)## Length Class Mode
## sim 1000 -none- numeric
## obs 1 -none- numeric
## rep 1 -none- numeric
## pvalue 1 -none- numeric
## call 4 -none- call#RMSE
rmse <- function(x, dis.matrix){
r.m.s.e <- 1- (mean(as.vector(x)^2))/var(as.vector(dis.matrix))
return(r.m.s.e)
}
bray.co.resid <- bray.ave.cov - bray.v
rmse(bray.co.resid, bray.v)## [1] 0.8601001Agglomerative, Hierarchical Clustering based on Standardized Data, Chord
library(scales)
library(ggplot2)
library(plyr)
library(vegan)
library(cluster)
library(ade4)
library(stats)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & !(Y$Var=="Z"))
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwghttrial),
meanwght = mean(rtwghttrial, na.rm=TRUE),
meanlngth = mean(rtlngthtrial, na.rm=TRUE),
meanarea = mean(rtareatrial, na.rm=TRUE),
meanemerge = mean(emergetrial, na.rm=TRUE)))
Dend<-scale(as.matrix(Y4 [,c("meanwght", "meanlngth", "meanarea", "meanemerge")]))
rownames(Dend)=Y4$Title
chord = function (s,diag) {
# G.Nesslage, B.Maurer
# computes chord distance
# diag == TRUE returns full distance matrix
# diag == FALSE returns distance matrix as dist object
n = nrow(s)
p <- ncol(s)
D = matrix(0,n,n)
m <- 1
for (i in 1:(n-1)){
for (j in (i+1):n){
nd <- matrix(0,p,1)
dd1 <- matrix(0,p,1)
dd2 <- matrix(0,p,1)
for (k in 1:p){
nd[k,]<- s[i,k]*s[j,k]
dd1[k,] <- (s[i,k])^2
dd2[k,] <- (s[j,k])^2
}
D[i,j] <- sqrt(2*(1-(sum(nd)/sqrt(sum(dd1)*
sum(dd2)))))
D[j,i] <- D[i,j]
}
}
if(diag==FALSE) {D=as.dist(D)}
return (D)
}
rownames(Dend)=Y4$Title
chord1 <- chord(Dend,diag=FALSE)
chord.v <- as.vector(chord1)
label <- rownames(Dend)
chord.dis <- agnes(chord1, method="average", diss = TRUE)
chord.disv<-as.vector(chord.dis)
# very simple dendrogram
plot(chord.dis, ask = FALSE, which.plots = 2,labels = label)
#Cophenetic Distance and Correlation
chord.ave.co <- cophenetic(chord.dis)
chord.ave.cov <- as.vector(chord.ave.co)
cor(chord.ave.cov, chord.v)## [1] 0.9652956plot(y = chord.ave.cov, x = chord.v)
#RMSE
rmse <- function(x, dis.matrix){
r.m.s.e <- 1- (mean(as.vector(x)^2))/var(as.vector(dis.matrix))
return(r.m.s.e)
}
chord.co.resid <- chord.ave.cov - chord.v
rmse(chord.co.resid, chord.v)## [1] 0.9319524#Bray doesn't work with negative values
#bray.dis <- vegdist(as.matrix(Dend), method = "bray")
#bray.ave.dis <- agnes(bray.dis, diss = TRUE)
#plot(bray.ave.dis, ask = FALSE, which.plots = 2,labels = label)
#canberra doesn't work with negative values
#bray.dis <- vegdist(as.matrix(Dend), method = "brayberra")
#bray.ave.dis <- agnes(bray.dis, diss = TRUE)
#plot(bray.ave.dis, ask = FALSE, which.plots = 2,labels = label)
#bray.ave.co <- cophenetic(bray.ave.dis)
#bray.ave.cov <- as.vector(bray.ave.co) # vectorize the cophenetic distancesAgglomerative, Hierarchical Clustering Dendogram based on Raw Data Relative to Trial
library(scales)
library(ggplot2)
library(plyr)
library(vegan)
library(cluster)
library(ade4)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & !(Y$Var=="Z"))
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwght),
meanwght = mean(rtwght, na.rm=TRUE),
meanlngth = mean(rtlngth, na.rm=TRUE),
meanarea = mean(rtarea, na.rm=TRUE)))
Dend<-as.matrix(Y4 [,c("meanwght", "meanlngth", "meanarea")])
rownames(Dend)=Y4$Title
bray <- (vegdist(Dend, method = "bray", diag = FALSE, upper = FALSE))
bray.v <- as.vector(bray)
#print(bray)
bray.v<-as.vector(bray)
#print(bray.v)
bray.dis <- agnes(bray, method="ward", diss = TRUE)
bray.disv<-as.vector(bray.dis)
# very simple dendrogram
plot(bray.dis)
#Cophenetic Distances and Correlation
bray.ave.co <- cophenetic(bray.dis)
bray.ave.cov <- as.vector(bray.ave.co) # vectorize the cophenetic distances
cor(bray.ave.cov, bray.v)## [1] 0.9740127plot(y = bray.ave.cov, x = bray.v)
#Mantel Test
mant1 <- mantel.rtest(bray, bray.ave.co, nrepet=1000)
plot(mant1)## Warning in hist.default(sim, plot = FALSE, nclass = nclass, xlim = xlim0):
## argument 'xlim' is not made use of
mant1## Monte-Carlo test
## Observation: 0.9740127
## Call: mantelnoneuclid(m1 = m1, m2 = m2, nrepet = nrepet)
## Based on 1000 replicates
## Simulated p-value: 0.000999001#RMSE
rmse <- function(x, dis.matrix){
r.m.s.e <- 1- (mean(as.vector(x)^2))/var(as.vector(dis.matrix))
return(r.m.s.e)
}
bray.co.resid <- bray.ave.cov - bray.v
rmse(bray.co.resid, bray.v)## [1] -6.608387Divisive Clustering Raw Data Relative to Trial
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA"))
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwght),
meanwght = mean(rtwght, na.rm=TRUE),
meanlngth = mean(rtlngth, na.rm=TRUE),
meanarea = mean(rtarea, na.rm=TRUE), meanemerge = mean(Emerge, na.rm=TRUE)))
Dend<-scale(as.matrix(Y4 [,c("meanwght", "meanlngth", "meanarea", "meanemerge")]))
rownames(Dend)=Y4$Title
k.pot <- kmeans(as.matrix(Dend),5,100)
k.pot$cluster## NTC, No Rice NTC, Rice
## 1 1
## Phy. cucurbitacearum Phy. vexans
## 4 3
## Py. acanthicum Py. aff. diclinum
## 4 3
## Py. aff. dissotocum Py. aff. torulosum
## 1 4
## Py. aphanidermatum Py. attrantheridium
## 5 4
## Py. CAL_2011f Py. coloratum
## 2 4
## Py. conidiophorum Py. deliense
## 4 1
## Py. heterothallicum Py. inflatum
## 4 3
## Py. irregulare Py. lutarium
## 2 1
## Py. myriotylum Py. oopapillum
## 5 1
## Py. pachycaule Py. paroecandrum
## 1 3
## Py. perplexum Py. rostratifingens
## 1 1
## Py. spinosum Py. sylvaticum
## 2 2
## Py. torulosum Py. ultimum
## 1 5
## Py. ultimum var. sporangiiferum Py. ultimum var. ultimum
## 5 5
## NTC, No Rice NTC, Rice
## 3 3
## Phy. cucurbitacearum Phy. vexans
## 3 3
## Py. acanthicum Py. aff. diclinum
## 3 3
## Py. aff. dissotocum Py. aff. torulosum
## 3 2
## Py. aphanidermatum Py. attrantheridium
## 5 3
## Py. CAL_2011f Py. coloratum
## 2 3
## Py. conidiophorum Py. deliense
## 3 3
## Py. heterothallicum Py. inflatum
## 3 2
## Py. irregulare Py. lutarium
## 2 3
## Py. myriotylum Py. oopapillum
## 5 3
## Py. pachycaule Py. paroecandrum
## 3 2
## Py. perplexum Py. rostratifingens
## 3 3
## Py. spinosum Py. sylvaticum
## 2 2
## Py. torulosum Py. ultimum
## 3 5
## Py. ultimum var. sporangiiferum Py. ultimum var. ultimum
## 5 5as.matrix(k.pot$cluster)## [,1]
## NTC, No Rice 1
## NTC, Rice 1
## Phy. cucurbitacearum 4
## Phy. vexans 3
## Py. acanthicum 4
## Py. aff. diclinum 3
## Py. aff. dissotocum 1
## Py. aff. torulosum 4
## Py. aphanidermatum 5
## Py. attrantheridium 4
## Py. CAL_2011f 2
## Py. coloratum 4
## Py. conidiophorum 4
## Py. deliense 1
## Py. heterothallicum 4
## Py. inflatum 3
## Py. irregulare 2
## Py. lutarium 1
## Py. myriotylum 5
## Py. oopapillum 1
## Py. pachycaule 1
## Py. paroecandrum 3
## Py. perplexum 1
## Py. rostratifingens 1
## Py. spinosum 2
## Py. sylvaticum 2
## Py. torulosum 1
## Py. ultimum 5
## Py. ultimum var. sporangiiferum 5
## Py. ultimum var. ultimum 5
## NTC, No Rice 3
## NTC, Rice 3
## Phy. cucurbitacearum 3
## Phy. vexans 3
## Py. acanthicum 3
## Py. aff. diclinum 3
## Py. aff. dissotocum 3
## Py. aff. torulosum 2
## Py. aphanidermatum 5
## Py. attrantheridium 3
## Py. CAL_2011f 2
## Py. coloratum 3
## Py. conidiophorum 3
## Py. deliense 3
## Py. heterothallicum 3
## Py. inflatum 2
## Py. irregulare 2
## Py. lutarium 3
## Py. myriotylum 5
## Py. oopapillum 3
## Py. pachycaule 3
## Py. paroecandrum 2
## Py. perplexum 3
## Py. rostratifingens 3
## Py. spinosum 2
## Py. sylvaticum 2
## Py. torulosum 3
## Py. ultimum 5
## Py. ultimum var. sporangiiferum 5
## Py. ultimum var. ultimum 5library(d3heatmap)
library(stats)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCup.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA"))
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwght),
meanwght = mean(rtwght, na.rm=TRUE),
meanlngth = mean(rtlngth, na.rm=TRUE),
meanarea = mean(rtarea, na.rm=TRUE), meanemerge = mean(Emerge, na.rm=TRUE)))
stuff<-cbind(Y4$meanwght, Y4$meanlngth, Y4$meanarea, Y4$meanemerge)
rownames(stuff)<-Y4$Title
d3heatmap(stuff, scale = "column", labCol=c("Root dry weight", "Root length", "Root area", "Emergence"), cexCol=0.6, cexRow = 0.8, distfun=chord, hclustfun=hclust, Colv=as.dendogram(hc.rows), dendrogram = "row",
color = "Blues")
as.dendogram(hc.rows)
library(pheatmap)
library(plyr)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & (Y$Var=="RH"))
Y3$tisolate<-paste(Y3$Title,Y3$Trt)
Y4<-( ddply(Y3, c("Var", "tisolate"), summarise,
N = length(rtwght),
meanwght = mean(rtwghttrial, na.rm=TRUE),
meanlngth = mean(rtlngthtrial, na.rm=TRUE),
meanarea = mean(rtareatrial, na.rm=TRUE), meanemerge = mean(emergetrial, na.rm=TRUE)))
stuff<-cbind(Y4$meanwght, Y4$meanlngth, Y4$meanarea, Y4$meanemerge)
rownames(stuff)<-Y4$tisolate
colnames(stuff)<-c("Root Dry Weight", "Root Length", "Root Area", "Emergence")
pheatmap(stuff, show_rownames = TRUE, show_colnames = TRUE, fontsize_row=9,
#cluster_rows=hc.rows,
clustering_method="average")
Heatmap of 4 Cup Assay Variables No Chord - default
library(pheatmap)
library(plyr)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & (Y$Var=="Z"))
Y3$tisolate<-paste(Y3$Title,Y3$Trt)
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwght),
meanwght = mean(rtwghttrial, na.rm=TRUE),
meanlngth = mean(rtlngthtrial, na.rm=TRUE),
meanarea = mean(rtareatrial, na.rm=TRUE), meanemerge = mean(emergetrial, na.rm=TRUE)))
stuff<-cbind(Y4$meanwght, Y4$meanlngth, Y4$meanarea, Y4$meanemerge)
rownames(stuff)<-Y4$Title
colnames(stuff)<-c("Root Dry Weight", "Root Length", "Root Area", "Emergence")
pheatmap(stuff, show_rownames = TRUE, show_colnames = TRUE,
#cluster_rows=hc.rows,
clustering_method="average")
Heatmap of 4 Cup Assay Variables with CHORD
library(pheatmap)
library(plyr)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & (Y$Var=="RH"))
Y3$tisolate<-paste(Y3$Title,Y3$Trt)
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwght),
meanwght = mean(rtwghttrial, na.rm=TRUE),
meanlngth = mean(rtlngthtrial, na.rm=TRUE),
meanarea = mean(rtareatrial, na.rm=TRUE), meanemerge = mean(emergetrial, na.rm=TRUE)))
stuff<-cbind(Y4$meanwght, Y4$meanlngth, Y4$meanarea, Y4$meanemerge)
rownames(stuff)<-Y4$Title
colnames(stuff)<-c("Root Dry Weight", "Root Length", "Root Area", "Emergence")
pheatmap(stuff, show_rownames = TRUE, show_colnames = TRUE, fontsize_row=9,
#cluster_rows=hc.rows,
clustering_method="average")
library(pheatmap)
library(plyr)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCupApr19.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & (Y$Var=="Z"))
Y3$tisolate<-paste(Y3$Title,Y3$Trt)
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwght),
meanwght = mean(rtwghttrial, na.rm=TRUE),
meanlngth = mean(rtlngthtrial, na.rm=TRUE),
meanarea = mean(rtareatrial, na.rm=TRUE), meanemerge = mean(emergetrial, na.rm=TRUE)))
stuff<-cbind(Y4$meanwght, Y4$meanlngth, Y4$meanarea, Y4$meanemerge)
rownames(stuff)<-Y4$Title
chord = function (s,diag) {
# G.Nesslage, B.Maurer
# computes chord distance
# diag == TRUE returns full distance matrix
# diag == FALSE returns distance matrix as dist object
n = nrow(s)
p <- ncol(s)
D = matrix(0,n,n)
m <- 1
for (i in 1:(n-1)){
for (j in (i+1):n){
nd <- matrix(0,p,1)
dd1 <- matrix(0,p,1)
dd2 <- matrix(0,p,1)
for (k in 1:p){
nd[k,]<- s[i,k]*s[j,k]
dd1[k,] <- (s[i,k])^2
dd2[k,] <- (s[j,k])^2
}
D[i,j] <- sqrt(2*(1-(sum(nd)/sqrt(sum(dd1)*
sum(dd2)))))
D[j,i] <- D[i,j]
}
}
if(diag==FALSE) {D=as.dist(D)}
return (D)
}
chord1 <- chord(stuff,diag=FALSE)
hc.rows<-hclust(chord1, method="average")
colnames(stuff)<-c("Root Dry Weight", "Root Length", "Root Area", "Emergence")
pheatmap(stuff, show_rownames = TRUE, show_colnames = TRUE,
cluster_rows=hc.rows,
clustering_method="average")
Seed Rot
library(pheatmap)
library(plyr)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllRot.csv"))
Y3<-subset(Y, !(Y$SevPercent=="NA") & (Y$Var=="Z"))
Y3$tisolate<-paste(Y3$Title,Y3$Trt)
Y4<-( ddply(Y3, c("Var", "tisolate"), summarise,
N = length(SevPercent),
meansp = mean(SevPercent, na.rm=TRUE),
means = mean(Sev, na.rm=TRUE)))
stuff<-as.data.frame(cbind(Y4$meansp, Y4$means))
rownames(stuff)<-Y4$tisolate
colnames(stuff)<-c("Severity, as Percent", "severity")
pheatmap(stuff, show_rownames = TRUE, show_colnames = TRUE,
#cluster_rows=hc.rows,
clustering_method="average")
Seed Rot Zorro, two temperatures
library(pheatmap)
library(plyr)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllRotMay9.csv"))
Y20<-subset(Y, #!(Y$SevPercent=="NA") &
(Y$Var=="Z") & Y$Temp=="20")
Y26<-subset(Y, #!(Y$SevPercent=="NA") &
(Y$Var=="Z") & Y$Temp=="26")
Sev26<-Y26$SevPercent
Y3<-cbind(Y20, Sev26)
Y3$tisolate<-paste(Y3$Title,Y3$Trt)
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(SevPercent),
sev20 = mean(SevPercent, na.rm=TRUE),
sev26 = mean(Sev26, na.rm=TRUE)))
stuff<-as.data.frame(cbind(Y4$sev20, Y4$sev26))
rownames(stuff)<-Y4$Title
colnames(stuff)<-c("Severity 20C", "Severity 26C")
pheatmap(stuff, show_rownames = TRUE, show_colnames = TRUE,
#cluster_rows=hc.rows,
clustering_method="average")
Seed Rot Red Hawk, two temperatures
library(pheatmap)
library(plyr)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllRotMay9.csv"))
Y20<-subset(Y, #!(Y$SevPercent=="NA") &
(Y$Var=="RH") & Y$Temp=="20")
Y26<-subset(Y, #!(Y$SevPercent=="NA") &
(Y$Var=="RH") & Y$Temp=="26")
Sev26<-Y26$SevPercent
Y3<-cbind(Y20, Sev26)
Y3$tisolate<-paste(Y3$Title,Y3$Trt)
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(SevPercent),
sev20 = mean(SevPercent, na.rm=TRUE),
sev26 = mean(Sev26, na.rm=TRUE)))
stuff<-as.data.frame(cbind(Y4$sev20, Y4$sev26))
rownames(stuff)<-Y4$Title
colnames(stuff)<-c("Severity 20C", "Severity 26C")
pheatmap(stuff, show_rownames = TRUE, show_colnames = TRUE,
#cluster_rows=hc.rows,
clustering_method="average")
library(scales)
library(ggplot2)
library(plyr)
library(vegan)
library(cluster)
library(ade4)
library(stats)
Y <- (read.csv("/Users/devonrossman/Desktop/CSVforR/DryBean/AllCup.csv"))
Y3<-subset(Y, !(Y$rtarea=="NA") & !(Y$rtwght=="NA") & !(Y$Var=="Z"))
Y4<-( ddply(Y3, c("Var", "Title"), summarise,
N = length(rtwghttrial),
meanwght = mean(rtwghttrial, na.rm=TRUE),
meanlngth = mean(rtlngthtrial, na.rm=TRUE),
meanarea = mean(rtareatrial, na.rm=TRUE),
meanemerge = mean(emergetrial, na.rm=TRUE)))
Dend<-scale(as.matrix(Y4 [,c("meanwght", "meanlngth", "meanarea", "meanemerge")]))
rownames(Dend)=Y4$Title
chord = function (s,diag) {
# G.Nesslage, B.Maurer
# computes chord distance
# diag == TRUE returns full distance matrix
# diag == FALSE returns distance matrix as dist object
n = nrow(s)
p <- ncol(s)
D = matrix(0,n,n)
m <- 1
for (i in 1:(n-1)){
for (j in (i+1):n){
nd <- matrix(0,p,1)
dd1 <- matrix(0,p,1)
dd2 <- matrix(0,p,1)
for (k in 1:p){
nd[k,]<- s[i,k]*s[j,k]
dd1[k,] <- (s[i,k])^2
dd2[k,] <- (s[j,k])^2
}
D[i,j] <- sqrt(2*(1-(sum(nd)/sqrt(sum(dd1)*
sum(dd2)))))
D[j,i] <- D[i,j]
}
}
if(diag==FALSE) {D=as.dist(D)}
return (D)
}
rownames(Dend)=Y4$Title
chord1 <- chord(Dend,diag=FALSE)
chord.v <- as.vector(chord1)
label <- rownames(Dend)
chord.dis <- agnes(chord1, method="average", diss = TRUE)
chord.disv<-as.vector(chord.dis)
# very simple dendrogram
plot(chord.dis, ask = FALSE, which.plots = 2,labels = label)
hc.rows<-hclust(chord1, method="average")
colnames(Dend)<-c("Root Dry Weight", "Root Length", "Root Area", "Emergence")
pheatmap(Dend, show_rownames = TRUE, show_colnames = TRUE,
cluster_rows=hc.rows,
clustering_method="average")
Y5<-( ddply(Y4, c(“Var”, “Title”), summarise,
scalewght=scale(meanwght, center=TRUE, scale=TRUE), scalelngth=scale(meanlngth, center=TRUE, scale=TRUE), scalearea=scale(meanarea, center=TRUE, scale=TRUE), scaleemerge=scale(meanemerge, center=TRUE, scale=TRUE))) sdrtwght = sd(rtwght), sdrtlngth=sd(rtlngth), sdrtarea=sd(rtarea), sdemerge=sd(Emerge), Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.